{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>房屋编码</th>\n",
       "      <th>小区</th>\n",
       "      <th>朝向</th>\n",
       "      <th>房屋单价</th>\n",
       "      <th>参考首付</th>\n",
       "      <th>参考总价</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>605093949</td>\n",
       "      <td>大望新平村</td>\n",
       "      <td>南北</td>\n",
       "      <td>5434</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>114.180964</td>\n",
       "      <td>22.603698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>605768856</td>\n",
       "      <td>通宝楼</td>\n",
       "      <td>南北</td>\n",
       "      <td>3472</td>\n",
       "      <td>7.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>114.179298</td>\n",
       "      <td>22.566910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>606815561</td>\n",
       "      <td>罗湖区罗芳村</td>\n",
       "      <td>南北</td>\n",
       "      <td>5842</td>\n",
       "      <td>15.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>114.158869</td>\n",
       "      <td>22.547223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>605147285</td>\n",
       "      <td>兴华苑</td>\n",
       "      <td>南北</td>\n",
       "      <td>3829</td>\n",
       "      <td>10.8</td>\n",
       "      <td>36.0</td>\n",
       "      <td>114.158040</td>\n",
       "      <td>22.554343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>606030866</td>\n",
       "      <td>京基东方都会</td>\n",
       "      <td>西南</td>\n",
       "      <td>47222</td>\n",
       "      <td>51.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>114.149243</td>\n",
       "      <td>22.554370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        房屋编码      小区  朝向   房屋单价  参考首付   参考总价          经度         纬度\n",
       "0  605093949   大望新平村  南北   5434  15.0   50.0  114.180964  22.603698\n",
       "1  605768856     通宝楼  南北   3472   7.5   25.0  114.179298  22.566910\n",
       "2  606815561  罗湖区罗芳村  南北   5842  15.6   52.0  114.158869  22.547223\n",
       "3  605147285     兴华苑  南北   3829  10.8   36.0  114.158040  22.554343\n",
       "4  606030866  京基东方都会  西南  47222  51.0  170.0  114.149243  22.554370"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"D:/01.csv\",engine=\"python\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.title(\"深圳二手房价格分布图\")\n",
    "plt.rcParams[\"font.sans-serif\"]=[\"SimHei\"] #设置字体\n",
    "plt.rcParams[\"axes.unicode_minus\"]=False #该语句解决图像中的“-”负号的乱码问题\n",
    "plt.scatter(df[\"经度\"], df[\"纬度\"], s=df[\"房屋单价\"]*0.005,c=df[\"参考总价\"],cmap=\"Reds\", alpha=0.8)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参考总价极差为：175.000000\n",
      "房屋单价极差为：91766.000000\n"
     ]
    }
   ],
   "source": [
    "# 极差\n",
    "def d_ran(df, *cols):\n",
    "    krange = []\n",
    "    for col in cols:\n",
    "        crange = df[col].max()-df[col].min()\n",
    "        krange.append(crange)\n",
    "    return(krange)\n",
    "\n",
    "res = d_ran(df,\"参考总价\",\"房屋单价\")\n",
    "print(\"参考总价极差为：%f\"%res[0])\n",
    "print(\"房屋单价极差为：%f\"%res[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb1456a0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"参考总价\"].hist(bins = 75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>频数</th>\n",
       "      <th>频率</th>\n",
       "      <th>累计频率</th>\n",
       "      <th>频率%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[191.25, 200.175)</th>\n",
       "      <td>15</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[42.5, 51.25)</th>\n",
       "      <td>12</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[33.75, 42.5)</th>\n",
       "      <td>9</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[182.5, 191.25)</th>\n",
       "      <td>5</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.546667</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[51.25, 60.0)</th>\n",
       "      <td>5</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.613333</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[25.0, 33.75)</th>\n",
       "      <td>5</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[165.0, 173.75)</th>\n",
       "      <td>5</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.746667</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[95.0, 103.75)</th>\n",
       "      <td>3</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.786667</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[156.25, 165.0)</th>\n",
       "      <td>3</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.826667</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[173.75, 182.5)</th>\n",
       "      <td>3</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[130.0, 138.75)</th>\n",
       "      <td>2</td>\n",
       "      <td>0.026667</td>\n",
       "      <td>0.893333</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[86.25, 95.0)</th>\n",
       "      <td>2</td>\n",
       "      <td>0.026667</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[138.75, 147.5)</th>\n",
       "      <td>1</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[121.25, 130.0)</th>\n",
       "      <td>1</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.946667</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[112.5, 121.25)</th>\n",
       "      <td>1</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[147.5, 156.25)</th>\n",
       "      <td>1</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[68.75, 77.5)</th>\n",
       "      <td>1</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.986667</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[103.75, 112.5)</th>\n",
       "      <td>1</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[77.5, 86.25)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[60.0, 68.75)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   频数        频率      累计频率    频率%\n",
       "[191.25, 200.175)  15  0.200000  0.200000  20.00\n",
       "[42.5, 51.25)      12  0.160000  0.360000  16.00\n",
       "[33.75, 42.5)       9  0.120000  0.480000  12.00\n",
       "[182.5, 191.25)     5  0.066667  0.546667   6.67\n",
       "[51.25, 60.0)       5  0.066667  0.613333   6.67\n",
       "[25.0, 33.75)       5  0.066667  0.680000   6.67\n",
       "[165.0, 173.75)     5  0.066667  0.746667   6.67\n",
       "[95.0, 103.75)      3  0.040000  0.786667   4.00\n",
       "[156.25, 165.0)     3  0.040000  0.826667   4.00\n",
       "[173.75, 182.5)     3  0.040000  0.866667   4.00\n",
       "[130.0, 138.75)     2  0.026667  0.893333   2.67\n",
       "[86.25, 95.0)       2  0.026667  0.920000   2.67\n",
       "[138.75, 147.5)     1  0.013333  0.933333   1.33\n",
       "[121.25, 130.0)     1  0.013333  0.946667   1.33\n",
       "[112.5, 121.25)     1  0.013333  0.960000   1.33\n",
       "[147.5, 156.25)     1  0.013333  0.973333   1.33\n",
       "[68.75, 77.5)       1  0.013333  0.986667   1.33\n",
       "[103.75, 112.5)     1  0.013333  1.000000   1.33\n",
       "[77.5, 86.25)       0  0.000000  1.000000   0.00\n",
       "[60.0, 68.75)       0  0.000000  1.000000   0.00"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 频率分布情况\n",
    "gcut = pd.cut(df[\"参考总价\"],20, right=False)\n",
    "price = pd.DataFrame({\"频数\":gcut.value_counts()})\n",
    "price = price.rename(columns={gcut.name: \"频数\"})\n",
    "price[\"频率\"] = price[\"频数\"]/price[\"频数\"].sum()\n",
    "price[\"累计频率\"] = price[\"频率\"].cumsum()\n",
    "price[\"频率%\"] = price[\"频率\"].apply(lambda x:\"%.2f\" % (x*100))\n",
    "price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xba224a8>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "price[\"频率\"].plot(kind=\"pie\",figsize=[4,4],grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
